In various types of computer systems, there may be a need to collect, maintain, and utilize confidential data. In some instances, users may be reluctant to share this confidential information over privacy concerns. These concerns extend not only to pure security concerns, such as concerns over whether third parties such as hackers may gain access to the confidential data, but also to how the computer system itself may utilize the confidential data. With certain types of data, users providing the data may be somewhat comfortable with uses of the data that maintain anonymity, such as the confidential data merely being used to provide broad statistical analysis to other users.
One example of such confidential data is salary/compensation information. It may be desirable for a service such as a social networking service to entice its members to provide information about their salary or other work-related compensation in order to provide members with insights into various metrics regarding salary/compensation, such as an average salary for a particular job type in a particular city. There are technical challenges encountered, however, in providing various metrics to members. One particular technical challenge is that it can be difficult to determine locations that are similar to ones that the member already resides in (or is already interested in). Common metrics for similarity between cities, such as population size, weather, etc., may or may not have relevance for job searching. Additionally, locations may be considered similar for some industries but not others. For example, Silicon Valley and Austin both have fairly large software technology presences, but San Diego does not. Despite the fact that San Diego may have a similar population as Silicon Valley and similar weather, for the software industry Austin is actually a closer match to Silicon Valley than San Diego is.
Similar technical issues arise with titles and peer company groups.